Data Scientist, Customer AI Engineering

Data Scientist, Customer AI Engineering London, England

Tractable
Full Time London, England 65000 - 80000 GBP ANNUAL Today
Job description

Who we are

Tractable is an Artificial Intelligence company bringing the speed and insight of Applied AI to visual assessment. Trained on millions of data points, our AI-powered solutions connect everyone involved in insurance, repairs, and sales of homes and cars – helping people work faster and smarter, while reducing friction and waste.

Founded in 2014, Tractable is now the AI tool of choice for world-leading insurance and automotive companies. Our solutions unlock the potential of Applied AI to transform the whole recovery ecosystem, from assessing damage and accelerating claims and repairs to recycling parts. They help make response to recovery up to ten times faster – even after full-scale disasters like floods and hurricanes.

Tractable has a world-class culture, backed up by our team, making us a global employer of choice!

We're a diverse team, uniting individuals of over 40 different nationalities and from varied backgrounds, with machine learning researchers and motor engineers collaborating together on a daily basis. We empower each team member to have tangible impact and grow their own scope by intentionally building a culture centred around collaboration, transparency, autonomy and continuous learning.


Please note that this is a 3 month contract engagement.


Engineering @ Tractable

The engineering team at Tractable plays a key part in bringing our AI to life. The way we build our product is by putting a team together with all the skills and context it needs to be able to solve real world problems. We believe strongly in cross functional teams and collaboration. This means you will be working alongside software engineers, machine learning engineers, data scientists and product managers and designers. You will be working out how we can best help thousands of people around the world recover faster from an accident. We're proud to see our products have a massive impact on the insurance market daily… and we know this is just the beginning.

The role:
You will be joining the Customer AI Engineering team. CAIE works directly with our business development, product and AI research teams to provide a comprehensive solution to our customers' business problems. We continuously learn how to best achieve our mission: we make the best of feedback and experiences brought in by everyone and we look forward to hearing about what you can bring.

What you will be working on:

  • Evaluate and fine-tune our machine learning models using statistical techniques
  • Work at a key intersection between customers, AI researchers, product engineering teams, and business development
  • Partner cross-functionally with product managers, core platform engineers, and AI researchers to onboard customers onto our platform
  • Build modules that help onboard new customers onto our AI platform

What you need to be successful;

  • Familiarity with Python and canonical data science libraries (numpy, pandas, scikit-learn)
  • Good background in applying data science and statistics fundamentals in industry setting
  • Experience in one or more of the following: data cleaning, feature engineering, data visualisation, metric selection and requirements extraction
  • Ability to manage a complex technical project involving multiple stakeholders
  • Excellent communication and interpersonal skills
  • Ability to work independently and effectively in a dynamic environment


Diversity commitment

At Tractable, we are committed to building a diverse team and inclusive workplace where people's varied backgrounds and experiences are valued and recognised.

We encourage applications from candidates of all backgrounds and offer equal opportunities without discrimination.

Data Scientist, Customer AI Engineering
Tractable

www.tractable.ai
London, United Kingdom
Alex Dalyac
Unknown / Non-Applicable
51 to 200 Employees
Company - Private
Enterprise Software & Network Solutions
2014
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